Understanding Soil Moisture Dynamics Using Observations And Climate Models
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Understanding Soil Moisture Dynamics Using Observations and Climate Models
With the objective to improve our understanding of soil moisture and its long term changes, I analyzed and compared climate model simulations with in situ soil moisture observations. Three studies were conducted to investigate soil moisture variations on seasonal to interannual scales and its long term changes. To investigate soil moisture evolutions on seasonal to interannual scales and the capacity of reanalysis systems to capture the observed characteristics, I analyzed newly updated 19 yr of Chinese soil moisture data and evaluated ERA40, NCEP/NCAR reanalysis (R-1), and NCEP/DOE reanalysis 2 (R-2). Over this region, soil moisture seasonality is in general not strong. Seasonal cycles and interannual variations exhibit considerably spatial diversity. R-2 generally exhibits improved interannual variability and better seasonal patterns of soil moisture than R-1 as a result of incorporating observed precipitation. ERA40 produces a better mean value of soil moisture for most Chinese stations and good interannual variability. In terms of temporal scale - an indicator of anomaly persistence, R-2 has a memory about twice that of the observations for the growing season. The unrealistic long temporal scale of R-2 can be attributed to the deep layer of the land surface model, which is too thick and dominates the soil moisture variability. The analysis highlights the importance of correct soil parameters to land surface processes and points out possible directions in which the reanalysis can be continuously improved to provide more realistic soil moisture outputs. Observations from Ukraine and Russia show significant increases in summer for the period from 1958-1999 that seem contradictory to the classic summer drying issue from early modeling studies. To see whether the latest climate models can capture the observed patterns, I calculated trends in soil moisture simulations from Intergovernmental Panel on Climate Change Fourth Assessment climate models. The upward trends in observations, which cannot be explained by precipitation and temperature changes alone, were found to be much larger than most trends in model realizations. Solar dimming is proposed to have played an important role in modulating soil moisture variations for these two regions. Further, a series off-line sensitivity experiments with a sophisticated land surface model were conducted to investigate possible contribution from solar dimming and elevated CO2 to the observed soil moisture trends for Ukraine and Russia. I demonstrate, by imposing a downward trend to shortwave radiation forcing to mimic the dimming, the observed soil moisture pattern can be essentially reproduced. On the other hand, the effects of elevated CO2 are relatively small for the study period. The results support the hypothesis that solar dimming may have played an important role in regional soil moisture changes.
Observation, Theory and Modeling of Atmospheric Variability
This book contains tutorial and review articles as well as specific research letters that cover a wide range of topics: (1) dynamics of atmospheric variability from both basic theory and data analysis, (2) physical and mathematical problems in climate modeling and numerical weather prediction, (3) theories of atmospheric radiative transfer and their applications in satellite remote sensing, and (4) mathematical and statistical methods. The book can be used by undergraduates or graduate students majoring in atmospheric sciences, as an introduction to various research areas; and by researchers and educators, as a general review or quick reference in their fields of interest.
Assimilation of Remote Sensing Data into Earth System Models
In the Earth sciences, a transition is currently occurring in multiple fields towards an integrated Earth system approach, with applications including numerical weather prediction, hydrological forecasting, climate impact studies, ocean dynamics estimation and monitoring, and carbon cycle monitoring. These approaches rely on coupled modeling techniques using Earth system models that account for an increased level of complexity of the processes and interactions between atmosphere, ocean, sea ice, and terrestrial surfaces. A crucial component of Earth system approaches is the development of coupled data assimilation of satellite observations to ensure consistent initialization at the interface between the different subsystems. Going towards strongly coupled data assimilation involving all Earth system components is a subject of active research. A lot of progress is being made in the ocean–atmosphere domain, but also over land. As atmospheric models now tend to address subkilometric scales, assimilating high spatial resolution satellite data in the land surface models used in atmospheric models is critical. This evolution is also challenging for hydrological modeling. This book gathers papers reporting research on various aspects of coupled data assimilation in Earth system models. It includes contributions presenting recent progress in ocean–atmosphere, land–atmosphere, and soil–vegetation data assimilation.